General Insurance & the Data Revolution.

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22-Nov-16

The general insurance market has always relied on data. The more that is known about the risks, the easier it is to decide upon the right premium, the right cover, the right approach etc. So when a development like ‘Dig Data’ emerges, the industry will certainly take notice.

Big Data analysis has already revolutionised many areas of modern life — from healthcare, to politics, to sport — but not, so far, the wider general insurance market. However, that is set to change.

From weather patterns to social media, new sources of data will help insurers streamline costs, be more targeted with the risks they choose to underwrite, detect new customers, predict fraud, and identify which claims have the potential to become expensive.

In particular, property insurance executives have “fairly aggressive plans” for developing Big Data analysis taken from ‘smart homes’, according to Klayton Southwood, Director of Risk Consulting at Willis Towers Watson, who recently conducted their own survey into the future of this latest industry revolution.

They found that fewer than half of the property and casualty insurance executives who took part currently use big data in the analysis in pricing, underwriting and risk selection. But more than 75% of the survey respondents expected that to change in the next two years.

Of course, data produced by customers is already used in some areas of insurance, such as car coverage, where telematics that monitor driving styles help cut premiums for safe drivers. But John Davies, Managing Director of Risk Finance at broker, Marsh, says customers will only willingly offer up personal information under two conditions: if it is easy to do so, and if they receive something in return. “In the insurance industry,” he says, “it’s still not easy.”

The reality is that Underwriters face various trials in the field of collecting Big Data - asking the wrong questions, or using the data in isolation, could lead to poor decisions, which is an area of concern to everyone, including the regulators. Customers fear that information gained may be used against them rather than for them, and this is potentially the biggest challenge for the insurers – convincing the end user that Big Data collection is in their interests.

Because of the vast volumes involved, the more insurers mine and use data, the more likely it is that decisions will be automated, when the human touch is still required on some level. During a terrorist siege in Australia, demand for local taxis spiked in response. And with it, the company’s online prices went up dramatically too. On the face of it, a simple case of supply and demand. But after the event, it was them who came ‘under fire’, given that people were desperately trying to flee danger; they seemingly had their safety held to ransom at a time critical moment. Unaware of the clear and present danger, the computer analytics, programmed around the numbers, were unable to factor in conscientiousness, or the right thing to do. Would premiums and cover spike in this way in response to say, the forecast of a large flood, or can insurance firms programme ethics into their analytics? Only time will tell, but in such an event, the use of Big Data would at least speed the claims process up for the majority, leaving ‘humans’ to deal with the exceptional cases, which must be to the benefit of all, especially in the field of fraudulent claims.

It’s also worth remembering that ‘insurers are people too’ ie end users with families and friends affected by this development, so it’s not all about the bottom line. And also bear in mind that if the industry does get it wrong, they will spend a lot of time with the Ombudsman and Lawyers - not their favourite place to be.

Last year, the Financial Conduct Authority (FCA) also waded into the debate, requesting information on how insurers will use Big Data, which could result in adjustments to policy, guidance, or other forms of intervention, as a result. They broadly found positive consumer outcomes had resulted from its use, allowing firms to develop new products, as well as reducing form-filling, streamlining sales and the claims process.

But they did identify two areas where the use of Big Data had the potential to leave some consumers worse off, with the extent of risk segmentation so that some categories of customers may find it harder to obtain insurance, and also, how it might enhance some firms’ ability to identify opportunities to charge certain customers more - concerns shared by the CII and others too.

While personal lines carriers remain the market’s predictive analytics leaders, standard commercial lines and specialty lines carriers are steadily advancing too. Where commercial lines products are individually underwritten due to the complexity in each risk, predictive modelling plays a different role, facilitating development of benchmark pricing for the Underwriter to use in their risk evaluation. Commercial insurers have gained experience using benchmarks for underwriting and pricing, and they are now seeing applications for predictive models across all lines of business and account sizes.

Regulatory and legal risks aside, one of the biggest challenges facing insurers is making sure they have the best people with the right skills to capture and analyse big data properly. Respondents to the Willis Towers Watson survey ranked this as the greatest Big Data challenge by far, including resource availability, training, skills and capabilities. Recruiting the right candidates could well be the deciding factor in the success of this enterprise, and consultants would do well to prepare themselves for the deluge of requests coming their way. Specialist recruitment consultancy, Aston Charles, has for some time been ‘match fit’ in this regard and is looking forward to the evolution in this particular field.

So where to next? The CII, in particular, is keen to ensure that insurance firms recognise the implications of their Big Data projects, not only for those who will benefit from them, but for those who may be worse off too. They must avoid social sorting, and the risk that the data sources and algorithms could lead to some discriminatory outcomes for certain sectors of society, something that has unfortunately tarnished the industry in the past.

Like it or not, Big Data is the way of the future. Insurers have not only coped but, on the whole, grown with challenges presented to them since its inception. Take, for example, the invention (and initial reaction) to cars, planes and the internet, all of which continue to evolve, as do the industries that service them. In terms of commerce, Big Data is no different from any of these. The only real difficulty, therefore, is just to keep apace.

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